EFRI BRAID: Brain-inspired Algorithms for Autonomous Robots (BAAR)

EFRI BRAID：自主机器人的类脑算法 (BAAR)

• 批准号: 2318065
• 负责人: Junmin Wang
• 金额: $ 200万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2318065&HistoricalAwards=false
• 关键词: EFRI; BRAID; Brain; inspired; Algorithms

Autonomous robots, such as self-driving vehicles (SDVs) and household collaborative robots (Cobots), possess great potential to benefit society and meet several important national needs. Although artificial intelligence (AI) has made substantial progress, the current data/computational efficiency and adaptability of autonomous robots pale in comparison to humans in performing routine sensorimotor tasks such as driving and cooking. Enabling such autonomous robots to continually learn from experience and persistently improve their efficiency and resilience in the real world as humans do is imperative for their widespread deployments. This project aims to develop novel computational algorithms for robot autonomy with principles and insights of neurobiological learning and brain intelligence. The outcomes could make a multifaceted and transformative impact on autonomous robots such as SDVs, Cobots, and other intelligent robotic systems in manufacturing and healthcare applications that face the same challenges of computational/data inefficiency and adaptation inflexibility.The project seeks to provide a paradigm shift in autonomous robotic systems by incorporating brain-inspired intelligence throughout their fundamental and core capabilities of perception, planning, and continual learning. Using convergent engineering-science approaches, the project aims to create a fundamental and innovative framework of brain-inspired perception, learning, and planning algorithms for autonomous robots. The framework will be applied to SDVs and Cobots as two representative and complementary engineering systems through combined theoretical and empirical studies. Integrating brain-inspired innovations, the work will adapt and engineer the general brain-inspired methods and algorithms to SDVs and Cobots for experimental validation of the effectiveness in data- and energy-efficiency, adaptability, and resiliency. It is expected that the findings will not only provide a significant leap to SDVs and Cobots toward their real-world deployments, but also have a transformative impact on other intelligent robotic systems such as those in manufacturing and healthcare domains by improving their data/computation efficiency, adaptation resiliency, and intelligence interpretability.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

自动驾驶汽车（SDV）和家庭协作机器人（Cobot）等自主机器人具有造福社会并满足多项重要国家需求的巨大潜力。 尽管人工智能（AI）已经取得了长足的进步，但在执行驾驶和烹饪等日常感觉运动任务时，自主机器人目前的数据/计算效率和适应性与人类相比相形见绌。 使此类自主机器人能够像人类一样不断从经验中学习并持续提高其在现实世界中的效率和弹性，对于其广泛部署至关重要。该项目旨在利用神经生物学学习和大脑智能的原理和见解，开发用于机器人自主的新型计算算法。 这些成果可能会对制造和医疗保健应用中的 SDV、协作机器人和其他智能机器人系统等自主机器人产生多方面的变革性影响，这些机器人面临着计算/数据效率低下和适应不灵活的同样挑战。该项目旨在通过将受大脑启发的智能融入到其感知、规划和预测等基本和核心功能中，从而实现自主机器人系统的范式转变。 不断学习。该项目旨在利用融合工程科学方法，为自主机器人创建一个受大脑启发的感知、学习和规划算法的基本创新框架。通过理论和实证研究相结合，该框架将应用于 SDV 和 Cobots 作为两个具有代表性和互补性的工程系统。这项工作将整合类脑创新，将通用的类脑方法和算法调整和设计到 SDV 和协作机器人中，以通过实验验证数据和能源效率、适应性和弹性的有效性。 预计这些发现不仅将为 SDV 和协作机器人在现实世界的部署提供重大飞跃，而且还将通过提高数据/计算效率、适应弹性和智能可解释性，对其他智能机器人系统（例如制造和医疗保健领域的智能机器人系统）产生变革性影响。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优势和知识进行评估，被认为值得支持。 更广泛的影响审查标准。